Find that Trendy New Bikini this summer by Shopping Online

Techumble — Wed, 18 Nov 2020 12:16:34 +0000

Online fashion shopping has become the wave of the future, and the variety just keeps getting better and better, especially for all the beautiful plus-size ladies. There's nothing wrong with purchasing from the old-fashioned brick and mortar retail stores, but when you visit a website that features swimsuit styles that are made exclusively for your curvy shape, then the simple click of the computer mouse does all the work. Online shopping is quick, easy and so convenient, and you're getting the best in on-trend looks, a range in sizes, fabrics, colors and prices. In addition, you don't have to wait long if your item is sold out, because e-commerce re-stocks quickly.

Plus-size women have finally been heard and included at New York Fashion Week and beyond the global runways, because celebrating bigger girls with fabulous bodies is what the world wants to see. Healthy, curvy women rock, and Sports Illustrated models like Ashley Graham have made the plus-size figure more popular than ever, and it's about time. The beautiful brunette model likes to call herself the "body diversity ambassador." She loves flaunting her shape in gorgeous bikinis and a variety of swimsuit styles. Whether it's a one-piece, a tankini, a sexy waist-high bikini or separates, plus-size fashion trends are incredibly flattering and waiting just for you to choose a couple and hit the beach, lake or your local pool. Shopping online for plus-size swimsuits is also great for your fashion budget, because e-commerce tends to offer better discounts and sales than retail stores in your city or town.

Ashley Graham even has her own line of super glamorous and sexy swimsuits available at swimsuits for all in sizes 8 to 34. These swimsuits are stunning and feature a variety of cool trends like mesh panels, lace-up fronts, metal stud embellishments and much more. Shopping for a swimsuit is not easy for any woman, but when you shop online, you’ll find the best selection of women’s plus size bikini options — for instance, plus-size swimsuits are available in designs that tone the tummy, flatter the hips, are made for longer torsos, feature excellent underwire support and even 4-way stretch fabrics that smooth the entire core. If you can't recall how amazing plus-size beauties look in swimwear, need we remind you to check out Ashley Graham posing in her famous swimsuit parade in sultry Turks &Caicos. When an online retailer like swimsuits for all is endorsed by a plus size fashion model, you know that trends are heading in the right direction.

If you need more inspiration for summer's sizzling swimsuits, then check out popular social media sites like Pinterest Plus Size Swimwear, where you will find tons of ideas in trends for the season, how your favorite models and non-models alike all look posing in these beautiful suits with the best patterns and colors hot for the summer. There is power in numbers, and you can clearly see that beauty is not defined by someone's waist size. All these women show confidence, attractiveness, sex appeal and glamour in a curve-hugging swimsuit. It's time to join the party.

Programming Languages: Julia Touts Its Speed Edge Over Python And R

Techumble — Fri, 26 Jun 2020 10:12:40 +0000

If you're a data scientist and need to analyze loads of CSV files for insights into, say, stock-price and market movements, the Julia programming language trumps machine-learning rivals Python and R, according to Julia supporters.

Machine learning has propelled Python upwards to make it probably the most popular programming language among developers these days, along with Java and JavaScript.

However, Julia, a young language with roots in MIT's Computer Science and Artificial Intelligence Lab (CSAIL), has also become one to watch, having found a core audience among data scientists.

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Julia is not among the top 10 programming languages that developers use but it is in the top 10 most-loved programming languages in this year's survey from Stack Overflow, putting it up there with Rust, TypeScript, Python, Kotlin, Go, Dart, C#, Swift, JavaScript and SQL.

Some languages such as Rust aren't widely used by developers but they are appreciated by programmers for qualities that excel in systems programming, versus application programming. For example, Microsoft is looking to Rust for the memory-safety features lacking in C and C++, which are extensively employed in Windows and other Microsoft projects.

Julia on the other hand has been adopted by some programmers for its C-like speed, but it has a much smaller ecosystem of packages than Python.

A recent update to Julia has improved multi-threading to offer more speed enhancements, and that's what Julia developers argue is giving it a sizable edge over Python and statistical programming language R at the task of parsing CSV files for data analysis.

According to Deepak Suresh, a machine-learning engineer at Julia Computing, multithreading capabilities give Julia libraries an advantage over both machine-learning rivals with a range of different datasets accessed from CSV files, or comma-separated values text files.

Suresh has benchmarked statistical programming language R's fread, Pandas' read_csv for Python, and Julia's CSV.Jl CSV parsers and reckons that Julia comes out on top.

"Julia's CSV.Jl is 1.5 to 5 times faster than Pandas even on a single core; with multithreading enabled, it is as fast or faster than R's read_csv," he notes.

The benchmarks were carried out on a machine with Ubuntu 18.04 powered by an Intel Xeon Silver 4114 processor running at 2.20GHz.

As he explains, Julia's CSV.Jl is the only tool that is "fully implemented in its higher-level language rather than being implemented in C and wrapped from R/Python".

The benchmarks are meant to demonstrate the speed of loading data in Julia and also indicate the performance of Julia code during data analysis.

One of the example benchmarks looks at Apple stock price states – open, high, low and close – using a 2.5GB dataset with 50 million rows and five columns.

"The single threaded CSV.Jl is about 1.5 times faster than R's fread from data.Table. With multithreading CSV.Jl is about 22 times faster. Pandas' read_csv takes 34s to read, this is slower than both R and Julia," Suresh declares.

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Another looks at performance with a mortgage risk dataset from Google-owned data-science platform, Kaggle, which contains mixed type dataset, with 356,000 rows and 2,190 columns.

"Pandas takes 119s to read in this dataset. Single-threaded fread is about twice faster than CSV.Jl. However, with more threads Julia is either as fast or slightly faster than R," says Suresh.

Another is the acquisition dataset from US mortgage lender, Fannie Mae, which has four million rows and 25 columns.

"Single-threaded data.Table is 1.25 times faster than CSV.Jl. But, the performance of CSV.Jl keeps increasing with more threads. CSV.Jl gets about 4 times faster with multi-threading," he says.

Julia Computing says, across all eight datasets, Julia's CSV.Jl is always faster than Pandas, and with multi-threading it is competitive with R's data.Table.

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Find that Trendy New Bikini this summer by Shopping Online

Programming Languages: Julia Touts Its Speed Edge Over Python And R